Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Exploiting structure in man-made environments
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
2012 (English)Doctoral thesis, monograph (Other academic)
Abstract [en]

Robots are envisioned to take on jobs that are dirty, dangerous and dull, the three D's of robotics. With this mission, robotic technology today is ubiquitous on the factory floor. However, the same level of success has not occurred when it comes to robots that operate in everyday living spaces, such as homes and offices.

A big part of this is attributed to domestic environments being complex and unstructured as opposed to factory settings which can be set up and precisely known in advance. In this thesis we challenge the point of view which regards man-made environments as unstructured and that robots should operate without prior assumptions about the world. Instead, we argue that robots should make use of the inherent structure of everyday living spaces across various scales and applications, in the form of contextual and prior information, and that doing so can improve the performance of robotic tasks.

To investigate this premise, we start by attempting to solve a hard and realistic problem, active visual search. The particular scenario considered is that of a mobile robot tasked with finding an object on an entire unexplored building floor. We show that a search strategy which exploits the structure of indoor environments offers significant improvements on state of the art and is comparable to humans in terms of search performance. Based on the work on active visual search, we present two specific ways of making use of the structure of space. First, we propose to use the local 3D geometry as a strong indicator of objects in indoor scenes. By learning a 3D context model for various object categories, we demonstrate a method that can reliably predict the location of objects. Second, we turn our attention to predicting what lies in the unexplored part of the environment at the scale of rooms and building floors. By analyzing a large dataset, we propose that indoor environments can be thought of as being composed out of frequently occurring functional subparts. Utilizing these, we present a method that can make informed predictions about the unknown part of a given indoor environment.

The ideas presented in this thesis explore various sides of the same idea: modeling and exploiting the structure inherent in indoor environments for the sake of improving robot's performance on various applications. We believe that in addition to contributing some answers, the work presented in this thesis will generate additional, fruitful questions.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2012. , vi, 103 p.
Series
Trita-CSC-A, ISSN 1653-5723 ; 2012:14
Keyword [en]
robotics, mapping, computer vision
National Category
Robotics
Identifiers
URN: urn:nbn:se:kth:diva-104410ISBN: 978-91-7501-549-1 (print)OAI: oai:DiVA.org:kth-104410DiVA: diva2:564466
Public defence
2012-11-20, F3, Lindstedtsvägen 26, KTH, Stockholm, 10:00 (English)
Opponent
Supervisors
Projects
CogX
Funder
EU, FP7, Seventh Framework Programme, ICT-215181
Note

QC 20121105

Available from: 2012-11-05 Created: 2012-11-01 Last updated: 2012-11-05Bibliographically approved

Open Access in DiVA

Alper_Aydemir_PhD_Thesis(6714 kB)654 downloads
File information
File name FULLTEXT01.pdfFile size 6714 kBChecksum SHA-512
ce32b381a08331bd7b4add7393c156f649ec8df3fff6c222b55a4c7a27608393dda284a6d8f6fe42c785ff2a75012c4308c0f2d0ed922330e0a998de18d0a636
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Aydemir, Alper
By organisation
Computer Vision and Active Perception, CVAP
Robotics

Search outside of DiVA

GoogleGoogle Scholar
Total: 654 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

isbn
urn-nbn

Altmetric score

isbn
urn-nbn
Total: 358 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf